Regular Testing for Non-Shuffled Data¶
In [1]:
"""
This file tests the model's performance on the testing dataset.
For CLS, this script returns the testing accuracy.
For Grasp, this script returns the testing accuracy and visualizes the
grasp prediction.
Comment or uncomment certain lines of code for swapping between
training CLS model and Grasping model.
E.g. Uncomment the lines with NO SPACE between '#' and the codes:
# Get test acc for CLS model
#accuracy, loss = get_test_acc(model)
# Get test acc for Grasp model
accuracy, loss = get_grasp_acc(model)
----->
# Get test acc for CLS model
accuracy, loss = get_test_acc(model)
# Get test acc for Grasp model
#accuracy, loss = get_grasp_acc(model)
"""
import torch
import os
import time
import sys
from utils.parameters import Params
from multi_task_models.grcn_multi_alex import Multi_AlexnetMap_v3
from training.single_task.evaluation import get_cls_acc, get_grasp_acc, visualize_grasp, visualize_cls
params = Params()
model_name = params.MODEL_NAME
weights_dir = params.MODEL_PATH
weights_path = os.path.join(weights_dir, model_name, model_name + '_final.pth')
# AlexNet with 1st, 2nd layer pretrained on Imagenet
model = Multi_AlexnetMap_v3().to('cuda')
model.load_state_dict(torch.load(weights_path))
model.eval()
path = params.TEST_PATH
# if len(sys.argv) > 1:
# path = params.TEST_PATH_SHUFFLE
# Visualize CLS predictions one by one
#visualize_cls(model)
# Visualize grasp predictions one by one
#visualize_grasp(model)
/home/billy/anaconda3/envs/gr_cls_multitask/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. warnings.warn( /home/billy/anaconda3/envs/gr_cls_multitask/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) /tmp/ipykernel_37298/1692255469.py:41: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model.load_state_dict(torch.load(weights_path))
In [2]:
def register_custom_hooks(model):
"""
Registers hooks on specific layers of the model.
Returns a dictionary to store activations and a list of hooks.
"""
layers_to_track = {
'rgb_features': 2,
'd_features': 2,
'features': 12,
'grasp': 4,
'cls': 4
}
activations = {layer_name: [] for layer_name in layers_to_track}
def hook_fn(layer_name):
def hook(module, input, output):
activations[layer_name].append(output.clone().detach())
return hook
hooks = []
for layer_name, layer_idx in layers_to_track.items():
if layer_name in ['rgb_features', 'd_features']:
block = getattr(model, layer_name)
else:
block = getattr(model, layer_name.split('_')[0])
hooks.append(block[layer_idx].register_forward_hook(hook_fn(layer_name)))
return hooks, activations
In [3]:
# Register hooks
hooks, activations = register_custom_hooks(model)
In [4]:
# Get test acc for CLS model
c_accuracy, c_loss = get_cls_acc(model, include_depth=True, seed=None, dataset=path, truncation=None)
Dataset size: 400 Training steps: 360 -- Val steps: 40
In [6]:
activations
Out[6]:
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2.6509e-01, 4.3772e-01],
[ 1.1443e-01, -6.5849e-01, -4.6432e-01, ..., 2.3581e-01,
4.6474e-01, 4.8898e-01]],
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-3.2654e+00, -1.5283e+00],
[-4.0736e-01, -2.2506e+00, -1.2511e+00, ..., -2.5893e+00,
1.6765e-01, 9.5765e-01]],
[[-1.6558e+00, -2.2056e+00, -2.8207e+00, ..., -2.6323e+00,
1.5009e-01, -1.3480e+00],
[-4.2627e+00, -3.2653e+00, -2.9495e+00, ..., -6.0339e+00,
-4.4940e+00, 8.8909e-02],
[-2.5495e+00, -1.4872e+00, -1.9362e+00, ..., 1.3930e+00,
-3.6788e+00, 1.5882e-01],
...,
[-4.4343e+00, -3.5852e+00, -5.9194e-01, ..., -6.2837e-01,
-2.9082e+00, -1.8064e+00],
[-4.7762e+00, -1.1989e+00, 1.6467e+00, ..., -2.7676e+00,
-2.6181e+00, -6.1259e-01],
[-2.9413e+00, -1.5257e+00, 9.0018e-01, ..., -1.4433e+00,
-5.9343e-01, 2.9322e-01]],
[[ 1.0903e+00, 5.5576e-01, 7.6175e-01, ..., -3.4668e-01,
8.0364e-01, -7.4958e-01],
[ 3.4802e+00, 3.3444e+00, 2.3234e+00, ..., 4.3800e+00,
7.0903e+00, 4.1279e+00],
[ 1.6500e+00, 1.4869e+00, 1.2937e+00, ..., -7.6272e-01,
2.8353e+00, 2.8842e+00],
...,
[ 2.9666e+00, 7.7466e-01, 7.4910e-01, ..., 5.1615e+00,
1.8434e+00, 3.8084e-02],
[ 3.2175e+00, 2.8830e+00, 2.8327e+00, ..., 4.3105e+00,
5.1028e-01, 1.7081e+00],
[ 1.9440e+00, 1.2168e+00, 2.3270e+00, ..., 5.1224e-01,
-5.1084e-01, 1.3141e+00]]],
[[[-1.4912e-01, 6.6034e-01, 3.4397e-01, ..., -1.7121e+00,
-1.0148e+00, -8.3546e-01],
[ 7.0080e-01, 6.0954e-01, 1.4586e+00, ..., -4.3937e-02,
-4.9815e-03, -1.1733e+00],
[ 1.0335e+00, 1.2103e-01, 1.3866e+00, ..., 4.4525e-01,
-1.4469e+00, -2.2790e+00],
...,
[-2.4109e-01, -4.0273e+00, -2.8938e-01, ..., 2.5656e+00,
-2.6642e-01, -1.4359e+00],
[-1.7359e+00, -1.4372e+00, 5.7562e-01, ..., 1.7548e+00,
-1.5464e+00, -5.4970e-01],
[-1.3875e+00, -2.6327e+00, -3.0269e+00, ..., -1.3512e+00,
-3.6524e+00, -2.1447e+00]],
[[-7.3563e-01, -1.4091e+00, -2.0667e+00, ..., -3.4132e+00,
-4.3444e+00, -2.4611e+00],
[-1.9359e+00, -3.6224e+00, -3.9164e+00, ..., -4.0319e+00,
-5.2214e+00, -4.6372e+00],
[-1.7538e+00, -3.4083e+00, -4.0539e+00, ..., -5.3380e+00,
-5.3486e+00, -3.8573e+00],
...,
[-5.2639e+00, -8.4820e+00, -6.2530e+00, ..., -2.2966e+00,
-7.3259e+00, -3.2992e+00],
[-8.5253e+00, -9.2612e+00, -9.6016e+00, ..., -5.7815e+00,
-9.6252e+00, -4.2693e+00],
[-1.9199e+00, -4.2055e+00, -3.6363e+00, ..., -2.5589e+00,
-4.5290e+00, -1.1186e+00]],
[[ 6.8526e-01, 1.1593e+00, 1.0828e+00, ..., 1.5568e+00,
1.7671e+00, 8.7544e-01],
[ 8.7651e-01, 1.3651e+00, 1.4274e+00, ..., 1.4774e+00,
1.7313e+00, 9.4515e-01],
[ 5.2672e-01, 3.1036e+00, 2.0939e+00, ..., 2.0629e+00,
2.0109e+00, 1.9190e+00],
...,
[-1.4176e-01, 2.6980e-01, -3.9748e+00, ..., -2.7504e+00,
2.6325e+00, 7.3889e-01],
[-1.1893e+00, 2.0433e-01, -1.2511e+00, ..., -2.2217e+00,
1.6751e+00, -3.3960e-03],
[-1.9456e-01, -4.4457e-01, 2.5470e-01, ..., 8.4567e-01,
1.5442e+00, -1.0048e+00]],
...,
[[-1.0103e+00, -1.1497e+00, -1.1012e+00, ..., -2.6311e+00,
-3.5239e+00, -1.2622e+00],
[-2.3461e+00, -2.8998e+00, -1.2548e+00, ..., -1.9725e+00,
-4.8713e+00, -3.6374e+00],
[-9.5276e-01, -1.5319e+00, -6.2087e-02, ..., -1.6674e+00,
-5.6903e+00, -3.3010e+00],
...,
[-2.0405e+00, -4.7495e+00, -9.0598e-01, ..., -3.0522e+00,
-7.0020e+00, -1.8308e+00],
[-2.5941e+00, -6.1921e+00, -4.2315e+00, ..., -5.1705e+00,
-7.6486e+00, -7.8338e-01],
[-2.1695e+00, -3.0287e+00, -2.4993e+00, ..., -2.9502e+00,
-4.2256e+00, 2.2913e-01]],
[[-1.5193e+00, -2.1687e+00, -3.1584e+00, ..., -2.9833e+00,
-3.1613e+00, -5.8463e-01],
[-4.3675e+00, -4.3796e+00, -4.8771e+00, ..., -3.8698e+00,
-6.0584e+00, -2.1069e+00],
[-4.0465e+00, -4.9392e+00, -5.1296e+00, ..., -3.1438e+00,
-5.5016e+00, -2.0857e+00],
...,
[-4.9222e+00, -2.6404e+00, 1.2333e+00, ..., -1.2372e+00,
-2.9559e+00, -5.2996e-01],
[-3.4664e+00, -1.7774e+00, 4.9405e-01, ..., -2.2431e+00,
-3.5921e+00, 4.1128e-01],
[-2.9237e+00, -2.7354e+00, -1.9839e+00, ..., -2.6607e+00,
-3.5603e+00, 1.5971e-01]],
[[ 8.9639e-01, -2.2453e-01, 5.2767e-01, ..., 4.2293e-01,
1.0635e+00, 6.2445e-01],
[ 1.7290e+00, 8.3029e-01, 5.7293e-01, ..., 1.0430e+00,
1.3436e+00, 3.0764e+00],
[ 1.3759e+00, 2.5423e-01, 1.0720e+00, ..., 1.6494e+00,
4.7331e-01, 7.3000e-01],
...,
[ 3.2105e+00, 2.3904e+00, 4.8189e+00, ..., 1.5533e+00,
3.4113e+00, 1.3687e+00],
[ 3.9112e+00, 3.4373e+00, 4.1045e+00, ..., 2.7478e+00,
1.0306e+00, 1.4749e+00],
[ 2.8751e+00, 2.4656e+00, 1.0316e+00, ..., 2.2391e+00,
4.1483e-01, 1.2804e+00]]],
[[[-4.2928e-02, 7.8236e-01, -2.2727e-01, ..., -1.2900e+00,
-9.8259e-01, -8.1830e-01],
[ 6.1219e-01, 3.6689e-01, -1.5465e-01, ..., -6.1295e-01,
2.1591e-01, -1.0299e+00],
[ 1.4175e+00, 6.3753e-01, 1.7070e+00, ..., -1.3227e+00,
-4.6416e-01, -1.9777e+00],
...,
[-3.6539e-01, -6.9425e-01, 1.2179e-01, ..., -2.2893e+00,
-2.2947e+00, 5.6512e-02],
[-2.3761e+00, -1.7090e+00, 4.9646e-01, ..., -2.3007e+00,
-2.6889e+00, 2.2316e-01],
[-2.3952e+00, -3.9543e+00, -2.9217e+00, ..., -4.1443e+00,
-3.5633e+00, -1.4462e+00]],
[[-6.5159e-01, -1.3977e+00, -2.5173e+00, ..., -2.9599e+00,
-4.5769e+00, -2.4057e+00],
[-1.8239e+00, -3.6984e+00, -4.2033e+00, ..., -3.0820e+00,
-4.7680e+00, -4.5736e+00],
[-1.5027e+00, -3.8209e+00, -4.3286e+00, ..., -3.3923e+00,
-3.9234e+00, -3.6472e+00],
...,
[-6.9676e+00, -9.0450e+00, -7.1346e+00, ..., -7.4509e+00,
-8.4332e+00, -1.7886e+00],
[-9.8806e+00, -1.0375e+01, -8.4711e+00, ..., -1.0779e+01,
-1.0976e+01, -3.6860e+00],
[-2.5743e+00, -4.8662e+00, -4.1210e+00, ..., -4.1347e+00,
-4.2144e+00, -5.9901e-01]],
[[ 6.5647e-01, 1.1940e+00, 1.0367e+00, ..., 1.5424e+00,
1.8108e+00, 8.3824e-01],
[ 8.8688e-01, 1.6980e+00, 1.7052e+00, ..., 1.0743e+00,
2.2097e+00, 8.8820e-01],
[ 6.0757e-01, 3.3258e+00, 1.9284e+00, ..., 1.2838e+00,
2.5490e+00, 2.0239e+00],
...,
[-1.3718e-01, -4.9950e-01, 3.6879e-01, ..., 9.4477e-01,
1.3233e+00, -1.6567e-01],
[-4.6313e-01, 5.7185e-01, -6.0906e-01, ..., 5.8857e-01,
1.2777e+00, -3.7241e-01],
[ 6.3865e-01, 1.2027e+00, 9.4769e-01, ..., -2.5023e-01,
-4.5619e-01, -1.9276e+00]],
...,
[[-9.2861e-01, -1.2869e+00, -2.0111e+00, ..., -2.5916e+00,
-3.2442e+00, -1.1695e+00],
[-2.2541e+00, -3.3781e+00, -2.7399e+00, ..., -2.8773e+00,
-4.1523e+00, -3.4240e+00],
[-5.4477e-01, -1.4612e+00, -8.7932e-01, ..., -2.8923e+00,
-4.1805e+00, -3.0109e+00],
...,
[-2.8072e+00, -3.8164e+00, -1.6178e+00, ..., -4.9840e+00,
-6.2170e+00, -2.6052e+00],
[-3.9525e+00, -5.9114e+00, -2.9897e+00, ..., -4.5492e+00,
-5.2176e+00, -1.2249e+00],
[-3.2556e+00, -3.4859e+00, -3.0427e+00, ..., -2.6490e+00,
-2.5265e+00, 4.8852e-01]],
[[-1.4800e+00, -2.3327e+00, -3.4808e+00, ..., -2.8850e+00,
-2.7371e+00, -5.1959e-01],
[-4.3524e+00, -4.7397e+00, -4.4816e+00, ..., -4.1724e+00,
-5.7617e+00, -1.9653e+00],
[-3.8600e+00, -4.9654e+00, -2.9627e+00, ..., -3.1594e+00,
-5.0355e+00, -2.1064e+00],
...,
[-4.5365e+00, -3.8529e+00, -4.1461e+00, ..., -3.9387e+00,
-2.4243e+00, -1.2077e+00],
[-3.5402e+00, -9.2289e-01, -1.9979e+00, ..., -2.0098e+00,
-8.8139e-01, 4.2296e-01],
[-3.7710e+00, -2.9622e+00, -2.9704e+00, ..., -1.8543e+00,
-1.1317e+00, 4.0530e-01]],
[[ 8.8640e-01, -1.1134e-01, 7.0328e-01, ..., 5.0502e-01,
1.3708e+00, 6.0550e-01],
[ 1.6110e+00, 8.0783e-01, 6.0898e-01, ..., 6.1226e-01,
1.8252e+00, 3.1972e+00],
[ 1.3133e+00, 4.3984e-01, 1.7565e+00, ..., -3.6103e-02,
1.0056e+00, 8.3213e-01],
...,
[ 4.3384e+00, 3.7342e+00, 2.3687e+00, ..., 3.9836e+00,
2.7906e+00, 5.5195e-01],
[ 4.5405e+00, 4.2490e+00, 3.5903e+00, ..., 2.2229e+00,
1.7309e+00, 1.3908e+00],
[ 2.8338e+00, 2.7338e+00, 2.0694e+00, ..., 5.0888e-01,
5.4094e-01, 1.0291e+00]]],
[[[ 2.2497e-01, 1.2886e+00, 4.4325e-01, ..., -9.7275e-01,
-1.9049e-01, 7.4127e-01],
[ 3.6447e-01, -4.6896e-02, 5.1436e-01, ..., 8.8340e-02,
-5.3915e-01, 1.0968e+00],
[-7.5900e-01, -2.3674e-01, 6.9906e-01, ..., 2.3690e+00,
-3.1331e-01, 6.9990e-01],
...,
[-1.0671e+00, -1.8770e+00, -2.7938e-01, ..., -2.2331e+00,
5.8125e-02, -6.6430e-01],
[-1.0649e+00, -9.1285e-01, 5.0016e-02, ..., -1.2249e+00,
-1.8172e-02, -1.6403e+00],
[-2.6588e+00, -5.0666e+00, -3.8448e+00, ..., -4.9817e+00,
-4.1047e+00, -3.8462e+00]],
[[-8.8455e-01, -2.3291e+00, -4.1577e+00, ..., -2.9039e+00,
-4.9959e+00, -2.6254e-01],
[-5.2126e+00, -6.4492e+00, -5.0261e+00, ..., -3.4782e+00,
-7.2798e+00, -1.5358e+00],
[-3.9099e+00, -4.4996e+00, -3.2312e+00, ..., -1.0651e+00,
-6.5267e+00, -1.5540e+00],
...,
[-5.8348e+00, -6.0020e+00, -3.2811e+00, ..., -5.0792e+00,
-4.9409e+00, -3.7322e+00],
[-7.0862e+00, -7.7706e+00, -6.0489e+00, ..., -7.0319e+00,
-8.0144e+00, -6.5363e+00],
[-2.5327e+00, -4.4605e+00, -4.1309e+00, ..., -4.0274e+00,
-4.1456e+00, -2.5325e+00]],
[[-3.5552e-01, 2.1170e+00, 1.2555e+00, ..., 1.5109e+00,
2.2179e+00, 9.1108e-01],
[-7.7434e-02, 9.6548e-01, 9.8901e-01, ..., 1.5416e+00,
1.4263e+00, -6.9729e-01],
[ 1.2246e-01, 1.6958e+00, 1.0281e+00, ..., 1.4215e+00,
2.2208e+00, -6.8668e-02],
...,
[ 1.0240e+00, 1.4775e+00, 9.7341e-01, ..., 6.1860e-01,
1.0811e+00, 1.8049e-01],
[ 8.2834e-01, 6.3766e-01, 6.9490e-01, ..., 4.1979e-01,
3.3960e-01, 2.1154e-01],
[ 1.3195e+00, 1.4522e+00, 1.0059e+00, ..., 1.7048e+00,
1.1284e+00, 1.3717e-01]],
...,
[[-9.2638e-01, -1.8779e+00, -1.9596e+00, ..., -2.4873e+00,
-2.9838e+00, -4.8435e-01],
[-3.4445e+00, -4.2199e+00, -2.6155e+00, ..., -2.2567e+00,
-3.8987e+00, -2.6654e+00],
[-1.7463e+00, -1.9391e+00, -8.7777e-01, ..., -2.0252e+00,
-3.2794e+00, -1.4591e+00],
...,
[-2.7692e+00, -2.5155e+00, -1.6666e+00, ..., -2.3922e+00,
-3.4609e+00, -1.6438e+00],
[-2.9204e+00, -3.0106e+00, -2.0454e+00, ..., -2.1329e+00,
-4.1469e+00, -2.3455e+00],
[-3.4638e+00, -3.7774e+00, -2.8145e+00, ..., -3.4046e+00,
-3.6649e+00, -1.6585e+00]],
[[-1.7117e+00, -2.1888e+00, -2.9444e+00, ..., -5.6989e-01,
-1.3505e+00, -1.3867e+00],
[-4.4894e+00, -3.3409e+00, -2.9870e+00, ..., -2.0387e+00,
-1.1659e+00, -1.0588e+00],
[-3.6508e+00, -2.6467e+00, -2.7110e+00, ..., -2.2292e-01,
-1.3391e+00, -1.0155e+00],
...,
[-4.3124e+00, -2.1295e+00, -5.1120e+00, ..., -2.2291e+00,
-3.0117e+00, -1.7178e+00],
[-3.7101e+00, -2.7462e-01, -3.5763e+00, ..., -1.0344e+00,
-3.0012e+00, -1.3944e+00],
[-4.3914e+00, -2.6304e+00, -2.6762e+00, ..., -2.0547e+00,
-3.2766e+00, -1.2702e+00]],
[[ 1.1155e+00, 4.7781e-01, 6.9865e-01, ..., -2.7432e-01,
-7.7933e-02, -5.5562e-01],
[ 3.7554e+00, 3.2621e+00, 2.2778e+00, ..., 3.6006e+00,
2.6904e+00, 2.1633e+00],
[ 1.5277e+00, 6.3760e-01, 7.7330e-02, ..., 3.1977e+00,
2.3773e+00, 1.3202e+00],
...,
[ 2.7527e+00, 1.3393e+00, 1.6745e+00, ..., 2.1867e+00,
1.9168e+00, 1.3121e+00],
[ 3.7104e+00, 3.5471e+00, 1.9435e+00, ..., 2.7493e+00,
2.2322e+00, 2.1319e+00],
[ 1.9390e+00, 8.4835e-01, 2.7712e-01, ..., -1.8809e-01,
-2.7781e-01, -1.3651e-01]]]], device='cuda:0')]}
In [5]:
for layer_name, acts in activations.items():
if len(acts) > 1:
acts = torch.cat(acts, dim=0) # Shape: [Batch x Channels x Height x Width]
avg_activation = acts.mean(dim=(0, 2, 3)) # Shape: [Channels]
top_indices = torch.topk(avg_activation, 10).indices
print(f"Top 10 feature maps for {layer_name}: {top_indices.tolist()}")
Top 10 feature maps for rgb_features: [47, 5, 43, 61, 46, 62, 17, 37, 38, 10] Top 10 feature maps for d_features: [8, 53, 40, 62, 47, 5, 17, 57, 23, 32] Top 10 feature maps for features: [24, 18, 38, 36, 9, 30, 45, 60, 15, 54] Top 10 feature maps for cls: [1, 15, 8, 12, 5, 3, 9, 11, 14, 10]
In [6]:
avg_activation
Out[6]:
tensor([-1.5345, -0.8570, -1.4611, -1.2415, -2.6134], device='cuda:0')
In [6]:
# Debugging: Check stored activations
for layer_name, acts in activations.items():
print(f"Layer: {layer_name}, Activations Count: {len(acts)}")
if len(acts) > 0:
print(f"Activation Shape: {acts[0].shape}")
Layer: rgb_features, Activations Count: 400 Activation Shape: torch.Size([1, 64, 27, 27]) Layer: d_features, Activations Count: 400 Activation Shape: torch.Size([1, 64, 27, 27]) Layer: features, Activations Count: 400 Activation Shape: torch.Size([1, 64, 13, 13]) Layer: grasp, Activations Count: 0 Layer: cls, Activations Count: 400 Activation Shape: torch.Size([1, 5, 224, 224])
In [6]:
# Get test acc for Grasp model
accuracy, loss = get_grasp_acc(model, include_depth=True, seed=None, dataset=path, truncation=None)
Dataset size: 400 Training steps: 360 -- Val steps: 40
In [7]:
# Debugging: Check stored activations
for layer_name, acts in activations.items():
print(f"Layer: {layer_name}, Activations Count: {len(acts)}")
if len(acts) > 0:
print(f"Activation Shape: {acts[0].shape}")
Layer: rgb_features, Activations Count: 800 Activation Shape: torch.Size([1, 64, 27, 27]) Layer: d_features, Activations Count: 800 Activation Shape: torch.Size([1, 64, 27, 27]) Layer: features, Activations Count: 800 Activation Shape: torch.Size([1, 64, 13, 13]) Layer: grasp, Activations Count: 400 Activation Shape: torch.Size([1, 16, 54, 54]) Layer: cls, Activations Count: 400 Activation Shape: torch.Size([1, 16, 54, 54])
In [8]:
for layer_name, acts in activations.items():
if len(acts) > 1:
acts = torch.cat(acts, dim=0) # Shape: [Batch x Channels x Height x Width]
avg_activation = acts.mean(dim=(0, 2, 3)) # Shape: [Channels]
top_indices = torch.topk(avg_activation, 10).indices
print(f"Top 10 feature maps for {layer_name}: {top_indices.tolist()}")
Top 10 feature maps for rgb_features: [47, 5, 43, 61, 46, 62, 17, 37, 38, 10] Top 10 feature maps for d_features: [8, 53, 40, 62, 47, 5, 17, 57, 23, 32] Top 10 feature maps for features: [24, 18, 38, 36, 9, 30, 45, 60, 15, 54] Top 10 feature maps for grasp: [2, 3, 7, 8, 10, 5, 12, 4, 1, 6] Top 10 feature maps for cls: [1, 15, 8, 12, 5, 3, 9, 11, 14, 10]
In [9]:
print('Grasp: %s' % accuracy, loss)
print('CLS: %s' % c_accuracy, c_loss)
Grasp: 81.5 0.0 CLS: 85.0 0.0
For Shuffled Data¶
One Horizontal Split¶
In [7]:
"""
This file tests the model's performance on the testing dataset.
For CLS, this script returns the testing accuracy.
For Grasp, this script returns the testing accuracy and visualizes the
grasp prediction.
Comment or uncomment certain lines of code for swapping between
training CLS model and Grasping model.
E.g. Uncomment the lines with NO SPACE between '#' and the codes:
# Get test acc for CLS model
#accuracy, loss = get_test_acc(model)
# Get test acc for Grasp model
accuracy, loss = get_grasp_acc(model)
----->
# Get test acc for CLS model
accuracy, loss = get_test_acc(model)
# Get test acc for Grasp model
#accuracy, loss = get_grasp_acc(model)
"""
import torch
import os
import time
import sys
from utils.parameters import Params
from multi_task_models.grcn_multi_alex import Multi_AlexnetMap_v3
from training.single_task.evaluation import get_cls_acc, get_grasp_acc, visualize_grasp, visualize_cls
params = Params()
model_name = params.MODEL_NAME
weights_dir = params.MODEL_PATH
weights_path = os.path.join(weights_dir, model_name, model_name + '_final.pth')
# AlexNet with 1st, 2nd layer pretrained on Imagenet
model = Multi_AlexnetMap_v3().to('cuda')
model.load_state_dict(torch.load(weights_path))
model.eval()
path = params.TEST_PATH_SHUFFLE
# if len(sys.argv) > 1:
# path = params.TEST_PATH_SHUFFLE
# Visualize CLS predictions one by one
#visualize_cls(model)
# Visualize grasp predictions one by one
#visualize_grasp(model)
/home/billy/anaconda3/envs/gr_cls_multitask/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. warnings.warn( /home/billy/anaconda3/envs/gr_cls_multitask/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) /tmp/ipykernel_6021/609354409.py:41: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model.load_state_dict(torch.load(weights_path))
In [8]:
def register_custom_hooks(model):
"""
Registers hooks on specific layers of the model.
Returns a dictionary to store activations and a list of hooks.
"""
layers_to_track = {
'rgb_features': 2,
'd_features': 2,
'features': 12,
'grasp': 4,
'cls': 4
}
activations = {layer_name: [] for layer_name in layers_to_track}
def hook_fn(layer_name):
def hook(module, input, output):
activations[layer_name].append(output.clone().detach())
return hook
hooks = []
for layer_name, layer_idx in layers_to_track.items():
if layer_name in ['rgb_features', 'd_features']:
block = getattr(model, layer_name)
else:
block = getattr(model, layer_name.split('_')[0])
hooks.append(block[layer_idx].register_forward_hook(hook_fn(layer_name)))
return hooks, activations
In [9]:
# Register hooks
hooks, activations = register_custom_hooks(model)
In [4]:
# Get test acc for CLS model
c_accuracy, c_loss = get_cls_acc(model, include_depth=True, seed=None, dataset=path, truncation=None)
Dataset size: 400 Training steps: 72 -- Val steps: 8
In [5]:
for layer_name, acts in activations.items():
if len(acts) > 1:
acts = torch.cat(acts, dim=0) # Shape: [Batch x Channels x Height x Width]
avg_activation = acts.mean(dim=(0, 2, 3)) # Shape: [Channels]
top_indices = torch.topk(avg_activation, 10).indices
print(f"Top 10 feature maps for {layer_name}: {top_indices.tolist()}")
Top 10 feature maps for rgb_features: [47, 5, 43, 61, 46, 62, 17, 37, 38, 10] Top 10 feature maps for d_features: [8, 53, 40, 62, 47, 5, 17, 32, 43, 23] Top 10 feature maps for features: [24, 18, 38, 36, 9, 54, 45, 30, 60, 15] Top 10 feature maps for cls: [8, 12, 15, 1, 5, 9, 3, 11, 0, 14]
In [10]:
# Get test acc for Grasp model
accuracy, loss = get_grasp_acc(model, include_depth=True, seed=None, dataset=path, truncation=None)
Dataset size: 400 Training steps: 72 -- Val steps: 8
In [12]:
for layer_name, acts in activations.items():
if len(acts) > 1:
acts = torch.cat(acts, dim=0) # Shape: [Batch x Channels x Height x Width]
avg_activation = acts.mean(dim=(0, 2, 3)) # Shape: [Channels]
top_indices = torch.topk(avg_activation, 10).indices
print(f"Top 10 feature maps for {layer_name}: {top_indices.tolist()}")
Top 10 feature maps for rgb_features: [47, 5, 43, 61, 46, 62, 17, 37, 38, 10] Top 10 feature maps for d_features: [8, 53, 40, 62, 47, 5, 17, 32, 43, 23] Top 10 feature maps for features: [24, 18, 38, 36, 9, 54, 45, 30, 60, 15] Top 10 feature maps for grasp: [2, 3, 7, 8, 10, 5, 12, 4, 1, 11]
In [13]:
print('Grasp: %s' % accuracy, loss)
print('CLS: %s' % c_accuracy, c_loss)
Grasp: 26.0 0.0 CLS: 52.5 0.0